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fine_tuned_model_v2
msmokov/fine_tuned_model_v2
fine_tuned_model_v2 is a text classification model from msmokov. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Oct 19, 2025
- Updated
- Oct 19, 2025
- SHA
- 8d6332b8
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Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
fine_tuned_model_v2
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3136
- Accuracy: 0.88
- F1: 0.8808
- Precision: 0.8972
- Recall: 0.88
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.6375 | 1.0 | 28 | 0.4198 | 0.86 | 0.8606 | 0.8849 | 0.86 |
| 0.3582 | 2.0 | 56 | 0.3136 | 0.88 | 0.8808 | 0.8972 | 0.88 |
| 0.1972 | 3.0 | 84 | 0.2985 | 0.88 | 0.8808 | 0.8972 | 0.88 |
Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cpu
- Datasets 4.2.0
- Tokenizers 0.21.4
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Oct 19, 2025
- Updated
- Oct 19, 2025